An Interpretable Fuzzy System Learned Through Online Rule Generation and Multiobjective ACO With a Mobile Robot Control Application.

Juang, C-F; Jeng, T-L; Chang, Y-C

An Interpretable Fuzzy System Learned Through Online Rule Generation and Multiobjective ACO With a Mobile Robot Control Application.

Juang, C-F Jeng, T-L Chang, Y-C

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Trans Cybern, 2016, 46, (12), pp. 2706-2718
Issue Date:: 2016-12

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Field	Value	Language
dc.contributor.author	Juang, C-F
dc.contributor.author	Jeng, T-L
dc.contributor.author	Chang, Y-C
dc.date.accessioned	2023-04-19T23:16:20Z
dc.date.available	2023-04-19T23:16:20Z
dc.date.issued	2016-12
dc.identifier.citation	IEEE Trans Cybern, 2016, 46, (12), pp. 2706-2718
dc.identifier.issn	2168-2267
dc.identifier.issn	2168-2275
dc.identifier.uri	http://hdl.handle.net/10453/169995
dc.description.abstract	This paper proposes a new multiobjective optimization approach to designing a fuzzy logic system (FLS) using process data and applies it to the wall-following control of a mobile robot. The objectives considered include both the interpretability and control performance of the FLS. It is assumed that no off-line training data are available in advance, and the rule base is initially empty. All rules are generated through an online clustering and fuzzy set merging (OCFM) algorithm using data generated online during the FLS evaluation process. The OCFM builds a reference rule base that flexibly partitions the input space with distinguishable fuzzy sets (FSs). Based on the reference rule base, a new multiobjective front-guided continuous ant-colony optimization (MO-FCACO) algorithm is proposed to optimize the FLS structure and parameters. In addition to the objective functions defined to evaluate the FLS control performance, a transparency-oriented objective function is defined with constraints imposed on the FS parameters to obtain an interpretable FLS with transparent FSs. The MO-FCACO solves the constrained multiobjective optimization problem by optimizing all of the free parameters in an FLS through ant-path selection, sampling operation, and front-guided optimization processes. The multiobjective FLS design approach is applied to control the orientation and moving speed of a mobile robot in performing the wall-following task. Optimization performance of the MO-FCACO is verified through comparisons with various multiobjective population-based optimization algorithms. Experimental results verify the effectiveness of the designed FLSs in controlling a real robot.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Trans Cybern
dc.relation.isbasedon	10.1109/TCYB.2015.2486779
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	An Interpretable Fuzzy System Learned Through Online Rule Generation and Multiobjective ACO With a Mobile Robot Control Application.
dc.type	Journal Article
utslib.citation.volume	46
utslib.location.activity	United States
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2023-04-19T23:16:18Z
pubs.issue	12
pubs.publication-status	Published
pubs.volume	46
utslib.citation.issue	12

Abstract:

This paper proposes a new multiobjective optimization approach to designing a fuzzy logic system (FLS) using process data and applies it to the wall-following control of a mobile robot. The objectives considered include both the interpretability and control performance of the FLS. It is assumed that no off-line training data are available in advance, and the rule base is initially empty. All rules are generated through an online clustering and fuzzy set merging (OCFM) algorithm using data generated online during the FLS evaluation process. The OCFM builds a reference rule base that flexibly partitions the input space with distinguishable fuzzy sets (FSs). Based on the reference rule base, a new multiobjective front-guided continuous ant-colony optimization (MO-FCACO) algorithm is proposed to optimize the FLS structure and parameters. In addition to the objective functions defined to evaluate the FLS control performance, a transparency-oriented objective function is defined with constraints imposed on the FS parameters to obtain an interpretable FLS with transparent FSs. The MO-FCACO solves the constrained multiobjective optimization problem by optimizing all of the free parameters in an FLS through ant-path selection, sampling operation, and front-guided optimization processes. The multiobjective FLS design approach is applied to control the orientation and moving speed of a mobile robot in performing the wall-following task. Optimization performance of the MO-FCACO is verified through comparisons with various multiobjective population-based optimization algorithms. Experimental results verify the effectiveness of the designed FLSs in controlling a real robot.

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http://hdl.handle.net/10453/169995